CAVE: We compare the fusion quality visually and also after averaging the quantitative metrics on five random images from the CAVE dataset [1]} at \(\text{SNR}_h\)  and \(\text{SNR}_\ell \)  of 30dB. The quality metrics for different methods are compared in table REF . A sample fusion result is shown in figure REF , where we have only displayed the 16th band. Our method shows less spectral distortion compared to competing methods (compare the zoomed regions) and also has lesser noise grains. We compare our method with a state-of-the-art deep learning method [2]}, which has been shown to be superior to fusion networks such as 3D-CNN [3]} and PCNN [4]}.
Note that MHF-net is able to preserve spectral quality but shows noise; the proposed method is better able to retain spectral information and sharp edges and suppress noise. This is reflected in the lower ERGAS and SAM indices (table REF ).
